#Ruchi Moondra
#Assignment: World Happiness Analysis
#Loading the data
worldh <- read.csv("C:/Users/Ruchi/Desktop/Ruchi/Rutgers/Multivariate/Dataset/WH_2017.csv")
#Loading packages required for the analysis
library(plyr)
library(plotly)
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library(dplyr)
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library(tidyverse)
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library(corrgram)
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library(corrplot)
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library(car)
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library(factoextra)
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library(corrplot)
library(mice)
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#View the data
View(worldh)
#Displays the first few rows of the dataset
head(worldh)
## Country Happiness.Rank Happiness.Score Whisker.high Whisker.low
## 1 Norway 1 7.537 7.594445 7.479556
## 2 Denmark 2 7.522 7.581728 7.462272
## 3 Iceland 3 7.504 7.622030 7.385970
## 4 Switzerland 4 7.494 7.561772 7.426227
## 5 Finland 5 7.469 7.527542 7.410458
## 6 Netherlands 6 7.377 7.427426 7.326574
## Economy..GDP.per.Capita. Family Health..Life.Expectancy. Freedom
## 1 1.616463 1.533524 0.7966665 0.6354226
## 2 1.482383 1.551122 0.7925655 0.6260067
## 3 1.480633 1.610574 0.8335521 0.6271626
## 4 1.564980 1.516912 0.8581313 0.6200706
## 5 1.443572 1.540247 0.8091577 0.6179509
## 6 1.503945 1.428939 0.8106961 0.5853845
## Generosity Trust..Government.Corruption. Dystopia.Residual
## 1 0.3620122 0.3159638 2.277027
## 2 0.3552805 0.4007701 2.313707
## 3 0.4755402 0.1535266 2.322715
## 4 0.2905493 0.3670073 2.276716
## 5 0.2454828 0.3826115 2.430182
## 6 0.4704898 0.2826618 2.294804
#Display the structure of the attributes
str(worldh)
## 'data.frame': 155 obs. of 12 variables:
## $ Country : Factor w/ 155 levels "Afghanistan",..: 105 38 58 133 45 99 26 100 132 7 ...
## $ Happiness.Rank : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Happiness.Score : num 7.54 7.52 7.5 7.49 7.47 ...
## $ Whisker.high : num 7.59 7.58 7.62 7.56 7.53 ...
## $ Whisker.low : num 7.48 7.46 7.39 7.43 7.41 ...
## $ Economy..GDP.per.Capita. : num 1.62 1.48 1.48 1.56 1.44 ...
## $ Family : num 1.53 1.55 1.61 1.52 1.54 ...
## $ Health..Life.Expectancy. : num 0.797 0.793 0.834 0.858 0.809 ...
## $ Freedom : num 0.635 0.626 0.627 0.62 0.618 ...
## $ Generosity : num 0.362 0.355 0.476 0.291 0.245 ...
## $ Trust..Government.Corruption.: num 0.316 0.401 0.154 0.367 0.383 ...
## $ Dystopia.Residual : num 2.28 2.31 2.32 2.28 2.43 ...
# Adding another column name "Continent"
worldh$Continent <- NA
# Deleting unnecessary columns (Whisker.high and Whisker.low)
worldh <- worldh[, -c(4,5)]
# Changing the name of columns
colnames (worldh) <- c("Country", "Happiness.Rank", "Happiness.Score",
"Economy", "Family", "Life.Expectancy", "Freedom", "Generosity",
"Trust", "Dystopia.Residual", "Continent")
# Adding the values for Continent name in the data.
worldh$Continent[which(worldh$Country %in% c("Israel", "United Arab Emirates", "Singapore", "Thailand", "Taiwan Province of China",
"Qatar", "Saudi Arabia", "Kuwait", "Bahrain", "Malaysia", "Uzbekistan", "Japan",
"South Korea", "Turkmenistan", "Kazakhstan", "Turkey", "Hong Kong S.A.R., China", "Philippines",
"Jordan", "China", "Pakistan", "Indonesia", "Azerbaijan", "Lebanon", "Vietnam",
"Tajikistan", "Bhutan", "Kyrgyzstan", "Nepal", "Mongolia", "Palestinian Territories",
"Iran", "Bangladesh", "Myanmar", "Iraq", "Sri Lanka", "Armenia", "India", "Georgia",
"Cambodia", "Afghanistan", "Yemen", "Syria"))] <- "Asia"
worldh$Continent[which(worldh$Country %in% c("Norway", "Denmark", "Iceland", "Switzerland", "Finland",
"Netherlands", "Sweden", "Austria", "Ireland", "Germany",
"Belgium", "Luxembourg", "United Kingdom", "Czech Republic",
"Malta", "France", "Spain", "Slovakia", "Poland", "Italy",
"Russia", "Lithuania", "Latvia", "Moldova", "Romania",
"Slovenia", "North Cyprus", "Cyprus", "Estonia", "Belarus",
"Serbia", "Hungary", "Croatia", "Kosovo", "Montenegro",
"Greece", "Portugal", "Bosnia and Herzegovina", "Macedonia",
"Bulgaria", "Albania", "Ukraine"))] <- "Europe"
worldh$Continent[which(worldh$Country %in% c("Canada", "Costa Rica", "United States", "Mexico",
"Panama","Trinidad and Tobago", "El Salvador", "Belize", "Guatemala",
"Jamaica", "Nicaragua", "Dominican Republic", "Honduras",
"Haiti"))] <- "North America"
worldh$Continent[which(worldh$Country %in% c("Chile", "Brazil", "Argentina", "Uruguay",
"Colombia", "Ecuador", "Bolivia", "Peru",
"Paraguay", "Venezuela"))] <- "South America"
worldh$Continent[which(worldh$Country %in% c("New Zealand", "Australia"))] <- "Australia"
worldh$Continent[which(is.na(worldh$Continent))] <- "Africa"
# Moving the Continent column at the second position.
worldh <- worldh %>% select(Country,Continent, everything())
str(worldh)
## 'data.frame': 155 obs. of 11 variables:
## $ Country : Factor w/ 155 levels "Afghanistan",..: 105 38 58 133 45 99 26 100 132 7 ...
## $ Continent : chr "Europe" "Europe" "Europe" "Europe" ...
## $ Happiness.Rank : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Happiness.Score : num 7.54 7.52 7.5 7.49 7.47 ...
## $ Economy : num 1.62 1.48 1.48 1.56 1.44 ...
## $ Family : num 1.53 1.55 1.61 1.52 1.54 ...
## $ Life.Expectancy : num 0.797 0.793 0.834 0.858 0.809 ...
## $ Freedom : num 0.635 0.626 0.627 0.62 0.618 ...
## $ Generosity : num 0.362 0.355 0.476 0.291 0.245 ...
## $ Trust : num 0.316 0.401 0.154 0.367 0.383 ...
## $ Dystopia.Residual: num 2.28 2.31 2.32 2.28 2.43 ...
#Converting the Continent values into factorial.
worldh$Continent <- as.factor(worldh$Continent)
# Finding the correlation between numerical columns
Num.cols <- sapply(worldh, is.numeric)
Cor.data <- cor(worldh[, Num.cols])
corrplot(Cor.data, method = 'color')
#Analysis: We can see there is an inverse correlation between "Happiness Rank" and all the other numerical variables. In other words, the lower the happiness rank, the higher the happiness score, and the higher the other seven factors that contribute to happiness. So let's remove the happiness rank, and see the correlation again.
# Create a correlation plot
newdatacor = cor(worldh[c(3:10)])
corrplot(newdatacor, method = "number")
#Analysis: In the above cor plot, Economy, life expectancy, and family play the most significant role in contributing to happiness.
#Trust and generosity have the lowest impact on the happiness score.
#Plotting ScatterPLot
plot_ly(data = worldh,
x=~Economy, y=~Happiness.Score, type = "scatter",
text = ~paste("Country:", Country)) %>%
layout(title = "Happiness and GDP",
xaxis = list(title = "GDP per Capita"),
yaxis = list(title = "Happiness Score"))
## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
#Analysis: This interactive scatterplot shows that there is a strong positive correlation between GDP and Happiness.
#Let's do multiple Regression
dat <- worldh[c("Happiness.Score","Economy","Generosity")]
head(dat)
## Happiness.Score Economy Generosity
## 1 7.537 1.616463 0.3620122
## 2 7.522 1.482383 0.3552805
## 3 7.504 1.480633 0.4755402
## 4 7.494 1.564980 0.2905493
## 5 7.469 1.443572 0.2454828
## 6 7.377 1.503945 0.4704898
plot(dat)
#It seems like there is a positive correlation between economy and happiness score but this is not true between happiness score
#and generosity.
#3D plot of same
scatter3D(dat$Generosity, dat$Economy, dat$Happiness.Score, phi = 0, bty = "g",
pch = 20, cex = 2, ticktype = "detailed",
main = "Happiness data", xlab = "Generosity",
ylab ="Economy", zlab = "Happiness.Score")
#From the scatter plot we cannot determine that combination of high economy and generosity leads to greater happiness score.
#This is something we have to conclude after analyzing the effect of these 2 taken together.
# Checking the outliers in the dataset using the boxplot.
names(worldh)[4] <- "Happiness_Score"
ggplot(worldh, aes(x=Continent, y= Happiness_Score, colour = Continent)) +
geom_boxplot() +
theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
labs(title = "Happiness Score Boxplot",
x = "Continent",
y = "Happiness Score")
##Checking for normality using shaprio test
qqPlot(worldh$Economy)
## [1] 155 93
shapiro.test(worldh$Economy)
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## Shapiro-Wilk normality test
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## data: worldh$Economy
## W = 0.96977, p-value = 0.00175
#p-value is greater than 0.05 implying that the data is not significantly different from normal distribution
qqPlot(worldh$Family)
## [1] 155 152
shapiro.test(worldh$Family)
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## Shapiro-Wilk normality test
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## data: worldh$Family
## W = 0.91152, p-value = 4.186e-08
qqPlot(worldh$Life.Expectancy)
## [1] 139 106
shapiro.test(worldh$Life.Expectancy)
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## Shapiro-Wilk normality test
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## data: worldh$Life.Expectancy
## W = 0.94602, p-value = 1.135e-05
qqPlot(worldh$Freedom)
## [1] 140 130
shapiro.test(worldh$Freedom)
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## Shapiro-Wilk normality test
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## data: worldh$Freedom
## W = 0.95945, p-value = 0.0001673
qqPlot(worldh$Generosity)
## [1] 114 81
shapiro.test(worldh$Generosity)
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## Shapiro-Wilk normality test
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## data: worldh$Generosity
## W = 0.95783, p-value = 0.0001184
qqPlot(worldh$Trust)
## [1] 26 151
shapiro.test(worldh$Trust)
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## Shapiro-Wilk normality test
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## data: worldh$Trust
## W = 0.83902, p-value = 9.204e-12
#Family,Life expectancy and trust variables are not normally distributed
####PCA################
act_col <- c(3, 5:10)
act_col
## [1] 3 5 6 7 8 9 10
happiness_new <- worldh[, act_col]
cor(happiness_new)
## Happiness.Rank Economy Family Life.Expectancy
## Happiness.Rank 1.0000000 -0.81324364 -0.73675268 -0.78071584
## Economy -0.8132436 1.00000000 0.68829631 0.84307664
## Family -0.7367527 0.68829631 1.00000000 0.61208006
## Life.Expectancy -0.7807158 0.84307664 0.61208006 1.00000000
## Freedom -0.5516078 0.36987339 0.42496576 0.34982679
## Generosity -0.1326198 -0.01901125 0.05169263 0.06319149
## Trust -0.4058423 0.35094410 0.23184139 0.27975198
## Freedom Generosity Trust
## Happiness.Rank -0.5516078 -0.13261979 -0.4058423
## Economy 0.3698734 -0.01901125 0.3509441
## Family 0.4249658 0.05169263 0.2318414
## Life.Expectancy 0.3498268 0.06319149 0.2797520
## Freedom 1.0000000 0.31608271 0.4991828
## Generosity 0.3160827 1.00000000 0.2941595
## Trust 0.4991828 0.29415945 1.0000000
happiness_pca <- prcomp(happiness_new,scale=TRUE)
summary(happiness_pca)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 1.9426 1.1630 0.82011 0.73722 0.60113 0.40173
## Proportion of Variance 0.5391 0.1932 0.09608 0.07764 0.05162 0.02305
## Cumulative Proportion 0.5391 0.7324 0.82844 0.90608 0.95770 0.98076
## PC7
## Standard deviation 0.36702
## Proportion of Variance 0.01924
## Cumulative Proportion 1.00000
(eigen_happiness <- happiness_pca$sdev^2)
## [1] 3.7738458 1.3526400 0.6725747 0.5434929 0.3613563 0.1613830 0.1347073
eigen_happiness
## [1] 3.7738458 1.3526400 0.6725747 0.5434929 0.3613563 0.1613830 0.1347073
names(eigen_happiness) <- paste("PC",1:7,sep="")
sumlambdas <- sum(eigen_happiness)
sumlambdas
## [1] 7
propvar <- eigen_happiness/sumlambdas
propvar
## PC1 PC2 PC3 PC4 PC5 PC6
## 0.53912084 0.19323429 0.09608209 0.07764184 0.05162233 0.02305471
## PC7
## 0.01924390
cumvar_happiness <- cumsum(propvar)
cumvar_happiness
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## 0.5391208 0.7323551 0.8284372 0.9060791 0.9577014 0.9807561 1.0000000
matlambdas <- rbind(eigen_happiness,propvar,cumvar_happiness)
rownames(matlambdas) <- c("Eigenvalues","Prop. variance","Cum. prop. variance")
round(matlambdas,4)
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Eigenvalues 3.7738 1.3526 0.6726 0.5435 0.3614 0.1614 0.1347
## Prop. variance 0.5391 0.1932 0.0961 0.0776 0.0516 0.0231 0.0192
## Cum. prop. variance 0.5391 0.7324 0.8284 0.9061 0.9577 0.9808 1.0000
summary(happiness_pca)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 1.9426 1.1630 0.82011 0.73722 0.60113 0.40173
## Proportion of Variance 0.5391 0.1932 0.09608 0.07764 0.05162 0.02305
## Cumulative Proportion 0.5391 0.7324 0.82844 0.90608 0.95770 0.98076
## PC7
## Standard deviation 0.36702
## Proportion of Variance 0.01924
## Cumulative Proportion 1.00000
happiness_pca$rotation
## PC1 PC2 PC3 PC4 PC5
## Happiness.Rank 0.4788810 -0.08240033 0.055747166 -0.0423546 0.05297915
## Economy -0.4551507 0.25969334 0.004308571 -0.2549601 -0.10093319
## Family -0.4133402 0.18852086 -0.186020546 0.3805632 0.72959861
## Life.Expectancy -0.4371899 0.23420596 -0.171512604 -0.3328363 -0.40772765
## Freedom -0.3364051 -0.40606620 0.218916193 0.6702964 -0.43207360
## Generosity -0.1052528 -0.67002041 -0.695293769 -0.2063681 0.05870829
## Trust -0.2779915 -0.47070149 0.633636921 -0.4309386 0.31355300
## PC6 PC7
## Happiness.Rank 0.853440489 0.16677560
## Economy 0.117920263 0.79767244
## Family 0.259451316 -0.12061794
## Life.Expectancy 0.393633806 -0.54094969
## Freedom 0.181306473 0.07183781
## Generosity 0.005882616 0.10243048
## Trust 0.050645253 -0.11435316
print(happiness_pca)
## Standard deviations (1, .., p=7):
## [1] 1.9426389 1.1630305 0.8201065 0.7372197 0.6011292 0.4017250 0.3670249
##
## Rotation (n x k) = (7 x 7):
## PC1 PC2 PC3 PC4 PC5
## Happiness.Rank 0.4788810 -0.08240033 0.055747166 -0.0423546 0.05297915
## Economy -0.4551507 0.25969334 0.004308571 -0.2549601 -0.10093319
## Family -0.4133402 0.18852086 -0.186020546 0.3805632 0.72959861
## Life.Expectancy -0.4371899 0.23420596 -0.171512604 -0.3328363 -0.40772765
## Freedom -0.3364051 -0.40606620 0.218916193 0.6702964 -0.43207360
## Generosity -0.1052528 -0.67002041 -0.695293769 -0.2063681 0.05870829
## Trust -0.2779915 -0.47070149 0.633636921 -0.4309386 0.31355300
## PC6 PC7
## Happiness.Rank 0.853440489 0.16677560
## Economy 0.117920263 0.79767244
## Family 0.259451316 -0.12061794
## Life.Expectancy 0.393633806 -0.54094969
## Freedom 0.181306473 0.07183781
## Generosity 0.005882616 0.10243048
## Trust 0.050645253 -0.11435316
happiness_pca$x
## PC1 PC2 PC3 PC4 PC5
## [1,] -3.57859076 -1.07900554 0.449013005 -0.1789550946 0.203059763
## [2,] -3.64618244 -1.48980003 0.990024008 -0.4608502434 0.573909482
## [3,] -3.21516819 -0.86940405 -1.236637827 0.4295796131 -0.057504412
## [4,] -3.62968569 -0.90598652 1.082899829 -0.4344483627 0.241576488
## [5,] -3.43368285 -0.85829325 1.429869160 -0.2687758954 0.449975887
## [6,] -3.16035632 -1.46201066 -0.328555808 -0.5222806932 0.034869374
## [7,] -3.28560398 -1.33882788 -0.131583784 -0.3238333967 0.059282840
## [8,] -3.57677808 -2.13014748 0.105154667 -0.6567639162 0.592413042
## [9,] -3.49956834 -1.54378394 0.741862247 -0.6603497428 0.328410764
## [10,] -3.36712682 -1.56090798 -0.298389383 -0.4704326429 0.206716019
## [11,] -1.89146538 0.54001301 -1.077606751 -0.3075777203 -0.263070983
## [12,] -1.84668885 0.35636402 -0.105505461 0.9083457772 -0.466672573
## [13,] -2.79237423 -0.36176314 0.041401371 -0.0658852016 -0.088381006
## [14,] -2.39916192 -0.20881363 -0.916697961 -0.1296829567 -0.184109722
## [15,] -3.26523471 -1.20263917 -0.100086896 -0.4336887413 0.433114686
## [16,] -2.90506434 -0.71664210 0.286038649 -0.3193487783 0.170536280
## [17,] -2.68665200 -0.02123863 0.628818619 -0.1796474566 0.060226074
## [18,] -3.37226839 -0.55115221 0.853494312 -0.5044411931 0.003137049
## [19,] -2.83869138 -1.30732108 -0.687932287 -0.7058434470 0.424782929
## [20,] -1.41918809 0.47802549 -1.037476067 -0.4497009465 -0.351629230
## [21,] -2.82447224 -1.31479390 0.716614012 -0.6150934463 -0.230138049
## [22,] -1.16451407 0.80030259 0.130516142 0.5166461105 0.251516346
## [23,] -1.53624301 1.65570184 0.031308277 0.8442186950 -0.452093769
## [24,] -1.33181556 1.27459580 0.107091847 0.9230011653 -0.222552066
## [25,] -1.00871753 0.94285709 0.522286962 -0.0762243420 -0.356430525
## [26,] -3.61831619 -1.42046514 1.369127960 -1.6878178770 0.192185459
## [27,] -2.60346850 -1.47688651 -1.691335457 0.0231127723 -0.134154767
## [28,] -1.89299703 0.18851224 0.635982360 0.6056855296 -0.189612638
## [29,] -0.65417688 -0.24797305 -0.393243481 0.9044988704 -0.320881822
## [30,] -1.47794544 0.55531392 -0.219829932 0.8289947492 -0.498043555
## [31,] -1.99212365 0.96902058 0.607000570 -0.1295288111 -0.237185195
## [32,] -1.55148595 -1.23419687 -2.276442976 0.7917838221 -0.286220885
## [33,] -1.43534318 1.07334010 -0.851359066 -0.2886707820 -0.122700987
## [34,] -1.83105589 1.41004449 -0.549688279 0.0881812476 -0.042525816
## [35,] -3.20075133 -1.56968139 1.610097003 -1.2013643237 0.129486212
## [36,] -0.79703459 0.96776524 -0.092367101 0.9897397046 -0.079930096
## [37,] -1.66551614 0.20129398 1.372031638 -0.5191461299 0.304637918
## [38,] -1.04702380 0.24183492 -1.102742908 0.9617409025 -0.232021779
## [39,] -1.80155406 0.02716588 0.651292246 -0.3460029121 -0.137015194
## [40,] -0.91643705 1.95923399 -0.577353743 0.1050966205 0.371143609
## [41,] -1.91561522 -0.03013221 1.202980084 -0.1176241826 0.013456985
## [42,] -0.93835238 -0.17505995 -1.395959938 -0.1647706719 -0.073508572
## [43,] -0.58530567 -0.33576072 -0.362186095 0.2275904509 0.028287419
## [44,] -0.84280002 0.54550115 0.470794033 0.2460946386 -0.151271947
## [45,] -0.17441884 0.98166526 0.628192537 0.5092685524 -0.358848890
## [46,] -1.32287800 0.99613248 -0.088869818 0.8807850976 -0.270599346
## [47,] -1.56548051 -1.96894618 0.025623351 1.0336480551 0.752459323
## [48,] -1.02931306 1.99551163 -0.849986193 -0.4690609301 0.110611821
## [49,] -0.54975375 1.89632080 0.179311508 0.7481764976 0.351493322
## [50,] -0.10770319 -0.38495000 0.179694864 0.8154683830 -0.598104104
## [51,] -2.00906491 1.01402221 0.594913430 0.0368696061 -0.338516257
## [52,] -0.30626081 2.67920504 0.003607533 0.1467905922 0.536153498
## [53,] 0.02588527 1.40016408 0.752302860 -0.8006129099 0.221427307
## [54,] -0.63166815 1.36162187 -0.174296089 0.0001178258 0.350049070
## [55,] -0.71913038 1.48977420 -0.623146362 -1.1600014202 -0.648640204
## [56,] 0.59692850 1.12916734 -0.851470784 -0.0079089301 0.248542490
## [57,] -0.36189391 1.27182198 -0.180236277 0.5317302292 -0.963852118
## [58,] -0.11082872 -0.11770014 -0.056839149 1.2389985644 -0.389824396
## [59,] -0.98489847 -0.45874536 0.709833059 -0.0473985221 1.304616725
## [60,] -0.91720786 0.56386753 0.064209941 0.2306271200 0.195240315
## [61,] -1.33555987 0.11545851 -0.031662720 -0.4928707486 -0.672457281
## [62,] -1.52801505 0.59772943 -0.585409784 0.8811346283 -0.560917982
## [63,] -0.19366186 0.99368154 0.110905915 0.6029463081 -0.495166180
## [64,] -0.61297600 -0.16425091 -1.063854732 0.3318621961 -0.599684897
## [65,] -0.67319291 0.90830926 -0.901750144 -0.6308319025 -0.842393336
## [66,] -1.37755476 0.82729656 0.939028050 0.2730222663 0.307097691
## [67,] -0.59575435 0.98895764 0.274504251 -0.3827137907 0.836595860
## [68,] -0.38878436 0.62892988 0.284154207 0.7346851340 0.142668841
## [69,] -0.23750484 1.72748114 0.561092208 -0.1217171325 0.319565230
## [70,] -0.57824668 0.30521353 -0.224895203 0.8816864072 0.468521131
## [71,] -2.26766775 -0.84938034 0.192306160 -1.3411506615 -0.284593363
## [72,] -0.15545867 -0.20919380 0.393974359 1.2567199316 -0.272985584
## [73,] 0.26648527 1.26041636 -0.788563334 -0.5989321997 0.289843681
## [74,] -0.17409237 0.45745866 0.302882708 0.1632081830 -0.040863027
## [75,] 0.05903833 2.29598688 -0.209045390 -0.5832185749 0.412109038
## [76,] -0.33197617 0.37519103 -0.443981039 0.7498006646 -0.090676301
## [77,] 0.33452834 0.93092983 -0.692061430 -0.9926451631 -0.682748842
## [78,] 0.58314487 0.27905443 -0.962872710 -0.5307628055 0.152427759
## [79,] -0.10269560 1.60241061 0.475974808 0.6817841380 -1.008908184
## [80,] 1.65067783 -0.52569174 -0.157235263 -1.2057548562 -0.456481633
## [81,] -0.06156536 -1.40892526 -2.512404729 0.2394920256 0.046607632
## [82,] 0.29079981 2.17075839 0.002738907 -0.4736455003 0.946715989
## [83,] 0.23374620 1.20623716 -0.385653862 -0.9171312566 0.385868835
## [84,] 0.95528913 0.92988070 1.127073631 -0.0789082453 -1.299423528
## [85,] -0.01294978 0.82009722 1.426935253 -0.1265528767 0.014506452
## [86,] -0.61830356 0.15813968 0.317390932 0.9581800194 0.019936013
## [87,] 0.32950064 2.90486958 0.113447802 -1.1721905755 0.168634474
## [88,] 0.24559822 0.81460027 -0.880930177 -0.6020821659 -0.386340879
## [89,] -0.73663365 1.55054859 -0.029805130 0.7699185416 -0.689563348
## [90,] 0.74987016 0.76586740 -1.507612532 -0.9012619240 -0.308836785
## [91,] 0.70128428 0.26860970 -0.323608229 -0.0055837434 -0.074890168
## [92,] 0.21536946 0.60967600 -0.609945679 -0.2823611999 -0.067468059
## [93,] 1.77607777 -2.84369936 1.672246024 0.6833648896 -0.233484007
## [94,] -0.18812117 -0.20843391 -0.035343368 0.9733861324 -0.464525643
## [95,] 1.58055347 -0.06025335 -0.183208747 1.2092278521 0.720584836
## [96,] 0.40550577 -0.57273804 0.205847645 0.5808786866 0.271113368
## [97,] -0.32807657 -1.66482410 -0.758504942 0.1740488023 0.512518641
## [98,] 0.31763161 -0.78037975 -1.505172879 0.7683671383 0.322768775
## [99,] 0.82251806 -0.98807412 -0.964680717 0.4676614686 0.021333727
## [100,] -0.05273460 0.19389174 -1.229961339 0.5241006045 0.580890276
## [101,] 0.62388282 0.34776390 0.508012972 1.4008505745 0.728460741
## [102,] 1.09942118 1.29185070 0.809408296 -0.6238623373 -0.752689428
## [103,] 1.12023428 1.01426108 0.288308146 -0.2669674840 0.266459312
## [104,] 1.00521750 0.77019321 0.549520524 -0.5853882128 -0.116687457
## [105,] 0.13334311 1.88281735 -0.420362235 0.1179282388 0.283636533
## [106,] 2.57469420 -0.84999467 -0.137953464 0.5892498983 0.718610429
## [107,] 2.04145380 -0.55883103 0.130249902 0.9102414959 -0.050471795
## [108,] 1.10977613 -0.09693228 -1.135804784 -1.5008363607 -1.078465629
## [109,] 0.86542303 0.56091616 -0.165689579 -0.4982266160 -1.451834146
## [110,] 1.35883007 -0.44017599 0.832847725 0.0578689887 -1.219182239
## [111,] 0.86081961 0.33814896 1.104860730 1.0453241874 -0.307614005
## [112,] 1.34738358 -1.47451269 -1.030803394 0.5448680342 0.028979708
## [113,] 2.08865072 -2.00573137 0.630966196 0.5921075370 0.177032308
## [114,] 0.55183758 -4.17156868 -2.293856130 -0.2421059723 0.444054020
## [115,] 1.35615544 -0.09187119 0.383802852 0.4500965647 0.423353296
## [116,] 1.59285208 -0.64501491 0.159500525 0.7634819166 -0.127968954
## [117,] 1.03354264 0.44766508 0.191230360 -0.7139680299 -0.153397200
## [118,] 1.10650532 1.33280785 0.827860567 0.1417263911 0.290635184
## [119,] 1.79986845 -1.40505687 0.322141350 -0.0791400991 -0.120796976
## [120,] -0.21459107 -1.33852695 -1.391473171 0.4529846546 -0.388206744
## [121,] 1.51521244 1.66387402 0.042250806 -0.6325094036 -0.300392207
## [122,] 1.43892688 -0.52584403 0.379371065 0.0620628581 -1.118821476
## [123,] 1.91327789 0.50785358 0.053139247 -0.7421947867 1.726155312
## [124,] 1.83486311 0.07159137 0.892826413 0.4519339686 -0.625017061
## [125,] 1.26881609 0.21659551 2.038510524 -1.6743249779 -1.076859518
## [126,] 2.64347168 -0.20711366 -0.357056245 0.5489262741 1.295403993
## [127,] 1.97238957 -0.04394473 0.337527277 0.6384363132 1.282533562
## [128,] 2.21146749 -0.92099559 0.941396371 0.7278300231 0.182928297
## [129,] 0.98345245 -1.61438997 -0.466739655 1.1365315716 -0.858873698
## [130,] 2.37518720 1.00413447 -0.215638919 -0.9729786728 1.654616555
## [131,] 2.06157488 -0.38843178 -0.188792818 0.5465048396 -0.560403533
## [132,] 1.22890977 1.12645653 -1.245347886 -0.6468514931 1.090057577
## [133,] 1.98155224 -1.02116536 -0.426608096 1.0168219553 0.365066781
## [134,] 2.30579180 -0.69970833 0.238917999 0.2382053163 0.661734024
## [135,] 2.37465573 -0.87923924 0.518214738 0.3430625446 0.423301845
## [136,] 2.81622514 -1.37160640 0.236038573 0.2479219275 -1.368156434
## [137,] 3.26673738 0.03810487 -0.045376385 -0.0789042527 0.963288203
## [138,] 2.38813295 -0.29412756 0.412581292 0.5524204241 0.655790520
## [139,] 2.28879123 -0.42997271 0.878680206 1.0719698515 1.135336469
## [140,] 3.02351471 1.35178692 0.332192021 -0.7621834317 1.699280417
## [141,] 3.65764812 -0.45726281 -0.429422385 -1.1794915517 0.017907334
## [142,] 0.83976623 0.36771883 0.960230241 0.9546267038 0.068322686
## [143,] 3.16815949 -0.85782974 0.648347013 0.1550807654 -1.375580499
## [144,] 2.83528246 0.14770720 -0.090942012 -0.4550997774 0.286495808
## [145,] 3.39869086 -1.20640549 -1.316955242 -1.9953016954 0.429055128
## [146,] 2.61049838 0.67610917 0.509489231 -0.0352743389 0.137672230
## [147,] 3.50623529 -0.71298832 0.119260545 -1.1451821612 0.148346569
## [148,] 3.11672979 -0.69073080 -0.221126791 0.4775553596 0.006091631
## [149,] 2.94328328 -1.07075480 0.340234851 0.1113525986 0.009353500
## [150,] 3.33086954 -0.89634966 0.838133192 -0.1659306678 -1.176304642
## [151,] 0.90890133 -2.93129847 2.696761035 -0.3454789097 0.531420637
## [152,] 2.71242548 -1.30599651 -0.934824563 -2.8828984344 -0.651856612
## [153,] 1.98262934 -0.92958240 -0.618511584 0.2775681081 0.075076935
## [154,] 4.24061565 -0.11655107 0.203365889 -1.0389509963 0.437649384
## [155,] 5.04420533 -1.54281984 0.449205665 -0.6897084890 -1.569703496
## PC6 PC7
## [1,] -0.193278348 0.186116013
## [2,] -0.172181065 -0.167388330
## [3,] -0.148434455 0.084584687
## [4,] -0.059863309 -0.152686457
## [5,] -0.131868540 -0.329984556
## [6,] -0.273250738 0.099234722
## [7,] -0.142311156 -0.039944517
## [8,] -0.059130911 -0.219963544
## [9,] -0.060672088 -0.140365759
## [10,] -0.045162961 -0.040299210
## [11,] -0.537082655 -0.137569790
## [12,] -0.474430284 -0.495376570
## [13,] -0.167896822 0.013520788
## [14,] -0.350806126 0.364060829
## [15,] 0.013156479 0.084711002
## [16,] -0.104216928 0.008229181
## [17,] -0.114115888 -0.135163735
## [18,] 0.128005879 0.326818335
## [19,] -0.092388291 0.012645203
## [20,] -0.551868609 -0.267821451
## [21,] -0.196052533 0.527361820
## [22,] -0.678138566 -0.263153265
## [23,] -0.334650185 -0.057422210
## [24,] -0.438445266 -0.246228590
## [25,] -0.673123040 -0.353408820
## [26,] 0.364472492 -0.070590320
## [27,] 0.097452031 0.048672773
## [28,] -0.174464087 -0.255861877
## [29,] -0.794560353 -0.260264670
## [30,] -0.276136008 -0.038219160
## [31,] -0.008462204 -0.196264120
## [32,] -0.285461377 0.214373048
## [33,] -0.236382139 0.100714799
## [34,] 0.117517903 -0.304710111
## [35,] 0.168782065 0.923190698
## [36,] -0.468588265 -0.137918913
## [37,] -0.353711199 0.528005369
## [38,] -0.454880796 0.797179815
## [39,] -0.210909645 0.794175687
## [40,] -0.264113185 -0.023098679
## [41,] -0.053922441 0.382974407
## [42,] -0.429221722 0.443728330
## [43,] -0.423995421 -0.820826587
## [44,] -0.270718033 -0.513527847
## [45,] -0.575603297 -0.443294964
## [46,] 0.054760571 0.075370909
## [47,] -0.037456516 -0.411964425
## [48,] 0.042234399 -0.152041873
## [49,] -0.317218260 0.283495782
## [50,] -0.664714591 0.117495964
## [51,] 0.564339600 -0.306564081
## [52,] -0.292703249 0.093755624
## [53,] -0.582210864 -0.267733858
## [54,] -0.168253394 0.088785754
## [55,] -0.009844272 -0.101147476
## [56,] -0.630939320 -0.662451468
## [57,] -0.152649722 0.145044211
## [58,] -0.367501250 -0.073342668
## [59,] -0.154728084 0.191483215
## [60,] 0.039953860 0.278527608
## [61,] 0.338720944 -0.013120756
## [62,] 0.590383391 0.122131642
## [63,] -0.106487132 -0.138329601
## [64,] 0.018077536 0.346762516
## [65,] 0.187413127 0.095062523
## [66,] 0.473302424 -0.002775801
## [67,] 0.089170435 -0.181086011
## [68,] -0.007236319 0.174643612
## [69,] 0.015394869 -0.065462690
## [70,] 0.228452326 -0.276232951
## [71,] 0.932945889 0.068066874
## [72,] -0.030232900 -0.029535364
## [73,] -0.127612630 -0.137884220
## [74,] 0.066194003 -0.193942698
## [75,] 0.061235244 0.041635210
## [76,] 0.301093229 -0.301690831
## [77,] -0.127683194 0.215326533
## [78,] -0.280845582 0.039237636
## [79,] 0.354157646 -0.257612646
## [80,] -0.954540549 0.043669223
## [81,] 0.043343159 0.545412232
## [82,] 0.099433223 -0.158946467
## [83,] 0.092818394 -0.108885105
## [84,] -0.240498389 -0.235426459
## [85,] 0.137086945 0.162175842
## [86,] 0.577179774 0.099462008
## [87,] 0.303005372 -0.248459151
## [88,] 0.279711377 -0.134052063
## [89,] 0.916953061 0.082833148
## [90,] 0.070157158 -0.157719551
## [91,] 0.088550829 -0.467063578
## [92,] 0.329947196 0.018053404
## [93,] -0.818051720 -0.626250051
## [94,] 0.674596139 -0.471114794
## [95,] -0.595514360 0.889470731
## [96,] 0.338744749 -0.783842741
## [97,] 0.524972052 0.105934275
## [98,] 0.482386426 -0.498876776
## [99,] 0.190242444 -0.583944337
## [100,] 0.656908535 0.184009935
## [101,] 0.084266594 0.975985097
## [102,] 0.105128030 -0.040636462
## [103,] 0.184945634 -0.571351883
## [104,] 0.108465434 0.116800482
## [105,] 0.838879489 -0.058494468
## [106,] -0.864297415 0.317525070
## [107,] -0.490735969 0.450666220
## [108,] 0.109917634 0.551397073
## [109,] 0.466802080 -0.065687540
## [110,] 0.138098063 -0.427881179
## [111,] 0.299291233 0.565643543
## [112,] 0.050221774 0.160915722
## [113,] -0.452224041 -0.115792885
## [114,] 0.382968464 -0.232439335
## [115,] 0.273880805 -0.547138793
## [116,] 0.011384763 0.306459823
## [117,] 0.346023696 0.495721485
## [118,] 0.317977481 0.864099704
## [119,] 0.001168142 -0.481115748
## [120,] 1.162528253 0.319529913
## [121,] 0.463609676 -0.236681338
## [122,] 0.288804793 0.252272601
## [123,] 0.021730864 -0.097740185
## [124,] 0.073556788 0.549716149
## [125,] 0.424162557 -0.193279842
## [126,] -0.139181074 -0.721667143
## [127,] 0.103166213 -0.026038480
## [128,] -0.199117047 0.704747772
## [129,] 0.745635607 0.092715666
## [130,] -0.007590484 -0.039829069
## [131,] 0.178338082 0.435322992
## [132,] 0.833621680 -0.119180769
## [133,] 0.281298097 -0.002299740
## [134,] 0.095054122 -0.202298447
## [135,] 0.158958570 -0.673166247
## [136,] -0.064028299 -0.270447092
## [137,] -0.424775457 0.382526225
## [138,] 0.181962451 -0.125137424
## [139,] 0.087634227 0.533086419
## [140,] -0.292788765 0.921759870
## [141,] -0.523352360 0.203246680
## [142,] 1.034660251 0.924362614
## [143,] -0.178776967 0.336050395
## [144,] 0.228037399 -0.595354299
## [145,] -0.307565614 -0.034378003
## [146,] 0.321276399 0.054379283
## [147,] -0.345268628 0.185689232
## [148,] 0.135651433 -0.441236917
## [149,] 0.112140708 -0.158756031
## [150,] -0.060479244 -0.028784797
## [151,] 0.997072472 -0.567340336
## [152,] 0.177626412 0.329349742
## [153,] 0.803875721 0.005725216
## [154,] -0.416996392 -0.419279115
## [155,] -0.968476275 0.168449208
happiness_new
## Happiness.Rank Economy Family Life.Expectancy Freedom
## 1 1 1.61646318 1.5335236 0.796666503 0.63542259
## 2 2 1.48238301 1.5511216 0.792565525 0.62600672
## 3 3 1.48063302 1.6105740 0.833552122 0.62716264
## 4 4 1.56497955 1.5169117 0.858131289 0.62007058
## 5 5 1.44357193 1.5402467 0.809157670 0.61795086
## 6 6 1.50394464 1.4289392 0.810696125 0.58538449
## 7 7 1.47920442 1.4813490 0.834557652 0.61110091
## 8 8 1.40570605 1.5481951 0.816759706 0.61406213
## 9 9 1.49438727 1.4781622 0.830875158 0.61292410
## 10 10 1.48441494 1.5100420 0.843886793 0.60160738
## 11 11 1.37538242 1.3762900 0.838404000 0.40598860
## 12 12 1.10970628 1.4164037 0.759509265 0.58013165
## 13 13 1.48709726 1.4599450 0.815328419 0.56776619
## 14 14 1.54625928 1.4199206 0.774286628 0.50574052
## 15 15 1.53570664 1.5582311 0.809782624 0.57311034
## 16 16 1.48792338 1.4725204 0.798950732 0.56251138
## 17 17 1.46378076 1.4623127 0.818091869 0.53977072
## 18 18 1.74194360 1.4575837 0.845089495 0.59662789
## 19 19 1.44163394 1.4964601 0.805335939 0.50819004
## 20 20 1.25278461 1.2840250 0.819479704 0.37689528
## 21 21 1.62634337 1.2664102 0.726798236 0.60834527
## 22 22 1.10735321 1.4313060 0.616552353 0.43745375
## 23 23 1.35268235 1.4338852 0.754444003 0.49094617
## 24 24 1.18529546 1.4404511 0.695137084 0.49451920
## 25 25 1.15318382 1.2108622 0.709978998 0.41273001
## 26 26 1.69227767 1.3538144 0.949492395 0.54984057
## 27 27 1.34327984 1.4884117 0.821944237 0.58876705
## 28 28 1.21755970 1.4122279 0.719216824 0.57939225
## 29 29 0.87200195 1.2555852 0.540239990 0.53131062
## 30 30 1.23374844 1.3731925 0.706156135 0.55002683
## 31 31 1.43092346 1.3877769 0.844465852 0.47022212
## 32 32 1.12786877 1.4257925 0.647239029 0.58020073
## 33 33 1.43362653 1.3845654 0.793984234 0.36146659
## 34 34 1.38439786 1.5320909 0.888960600 0.40878123
## 35 35 1.87076569 1.2742969 0.710098088 0.60413098
## 36 36 1.07062232 1.4021829 0.595027924 0.47748742
## 37 37 1.53062356 1.2866776 0.590148330 0.44975057
## 38 38 1.36135590 1.3802285 0.519983292 0.51863074
## 39 39 1.63295245 1.2596987 0.632105708 0.49633759
## 40 40 1.32539356 1.5050592 0.712732911 0.29581746
## 41 41 1.48841226 1.3231105 0.653133035 0.53674692
## 42 42 1.29121542 1.2846460 0.618784428 0.40226498
## 43 43 0.73729920 1.2872157 0.653095961 0.44755185
## 44 44 1.00082040 1.2861688 0.685636222 0.45519820
## 45 45 0.90978450 1.1821251 0.596018553 0.43245253
## 46 46 1.29178786 1.4457120 0.699475348 0.52034211
## 47 47 0.78644109 1.5489691 0.498272628 0.65824866
## 48 48 1.39506662 1.4449233 0.853144348 0.25645071
## 49 49 1.28177810 1.4692824 0.547349334 0.37378311
## 50 50 0.90797532 1.0814178 0.450191766 0.54750937
## 51 51 1.41691518 1.4363378 0.913475871 0.50562555
## 52 52 1.31458235 1.4735161 0.628949940 0.23423178
## 53 53 1.09186447 1.1462175 0.617584646 0.23333581
## 54 54 1.26074862 1.4047149 0.638566971 0.32570791
## 55 55 1.40167844 1.1282744 0.900214076 0.25792167
## 56 56 0.72887063 1.2518256 0.589465201 0.24072905
## 57 57 1.21768391 1.1500913 0.685158312 0.45700374
## 58 58 0.83375657 1.2276191 0.473630250 0.55873293
## 59 59 1.13077676 1.4931492 0.437726080 0.41827193
## 60 60 1.28455627 1.3843690 0.606041551 0.43745428
## 61 61 1.34691131 1.1863034 0.834647238 0.47120363
## 62 62 1.34120595 1.4525188 0.790828228 0.57257581
## 63 63 1.03522527 1.2187704 0.630166113 0.45000288
## 64 64 1.18939555 1.2095610 0.638007462 0.49124733
## 65 65 1.35593808 1.1313633 0.844714701 0.35511154
## 66 66 1.32087934 1.4766711 0.695168316 0.47913143
## 67 67 1.15655756 1.4449452 0.637714267 0.29540026
## 68 68 1.10180306 1.3575643 0.520169020 0.46573323
## 69 69 1.19827437 1.3377532 0.637605608 0.30074060
## 70 70 0.93253732 1.5072849 0.579250693 0.47350779
## 71 71 1.55167484 1.2627909 0.943062425 0.49096864
## 72 72 0.85769922 1.2539176 0.468009055 0.58521467
## 73 73 1.06931758 1.2581898 0.650784671 0.20871553
## 74 74 0.99101239 1.2390889 0.604590058 0.41842115
## 75 75 1.28601193 1.3431331 0.687763453 0.17586352
## 76 76 0.92557931 1.3682181 0.641022384 0.47430724
## 77 77 1.22255623 0.9679830 0.701288521 0.25577229
## 78 78 0.95148438 1.1378535 0.541452050 0.26028794
## 79 79 1.08116579 1.1608374 0.741415501 0.47278771
## 80 80 0.72688353 0.6726907 0.402047783 0.23521526
## 81 81 0.99553859 1.2744447 0.492345721 0.44332346
## 82 82 1.12843120 1.4313376 0.617144227 0.15399712
## 83 83 1.12112904 1.2383765 0.667464674 0.19498906
## 84 84 0.87811458 0.7748644 0.597710669 0.40815833
## 85 85 1.15360177 1.1524003 0.540775776 0.39815584
## 86 86 1.07937384 1.4024167 0.574873745 0.55258983
## 87 87 1.28948748 1.2394146 0.810198903 0.09573125
## 88 88 1.07498753 1.1296242 0.735081077 0.28851599
## 89 89 1.31517529 1.3670430 0.795843542 0.49846530
## 90 90 0.98240942 1.0693359 0.705186307 0.20440318
## 91 91 0.73057312 1.1439450 0.582569480 0.34807986
## 92 92 1.06457794 1.2078930 0.644948184 0.32590598
## 93 93 0.02264318 0.7211514 0.113989137 0.60212696
## 94 94 0.78854758 1.2774913 0.652168989 0.57105559
## 95 95 0.78375626 1.2157705 0.056915730 0.39495257
## 96 96 0.52471364 1.2714633 0.529235125 0.47156671
## 97 97 0.88541639 1.3401265 0.495879292 0.50153768
## 98 98 0.59622008 1.3942386 0.553457797 0.45494339
## 99 99 0.47982019 1.1792833 0.504130781 0.44030595
## 100 100 1.02723587 1.4930112 0.557783484 0.39414397
## 101 101 1.05469871 1.3847886 0.187080070 0.47924674
## 102 102 1.00726581 0.8683515 0.613212049 0.28968069
## 103 103 0.71624923 1.1556472 0.565666974 0.25471106
## 104 104 0.98970181 0.9974714 0.520187259 0.28211015
## 105 105 1.16145909 1.4343795 0.708217680 0.28923172
## 106 106 0.36842093 0.9841360 0.005564754 0.31869769
## 107 107 0.56430537 0.9460182 0.132892117 0.43038875
## 108 108 1.15687311 0.7115512 0.639333189 0.24932261
## 109 109 0.99619275 0.8036852 0.731159747 0.38149863
## 110 110 0.58668298 0.7351317 0.533241034 0.47835666
## 111 111 0.96443433 1.0984708 0.338611811 0.52030355
## 112 112 0.56047946 1.0679507 0.309988350 0.45276377
## 113 113 0.23430565 0.8707010 0.106654435 0.48079109
## 114 114 0.36711055 1.1232359 0.397522569 0.51449203
## 115 115 0.47930902 1.1796919 0.409362853 0.37792227
## 116 116 0.63640678 1.0031873 0.257835895 0.46160349
## 117 117 1.10271049 0.9786132 0.501180470 0.28855553
## 118 118 1.19821024 1.1556202 0.356578588 0.31232858
## 119 119 0.33923385 0.8646692 0.353409708 0.40884274
## 120 120 1.00985014 1.2599764 0.625130832 0.56121325
## 121 121 0.90059674 1.0074837 0.637524426 0.19830327
## 122 122 0.79222125 0.7543726 0.455427617 0.46998700
## 123 123 0.64845729 1.2720308 0.285349280 0.09609804
## 124 124 0.80896425 0.8320444 0.289957434 0.43502587
## 125 125 0.95061266 0.5706149 0.649546981 0.30941004
## 126 126 0.09210235 1.2290235 0.191407025 0.23596135
## 127 127 0.47618049 1.2814734 0.169365674 0.30661374
## 128 128 0.60304892 0.9047800 0.048642170 0.44770619
## 129 129 0.60176510 1.0062383 0.429783404 0.63337582
## 130 130 0.65951669 1.2140086 0.290920824 0.01499586
## 131 131 0.66722482 0.8736647 0.295637727 0.42302629
## 132 132 0.89465195 1.3945376 0.575903952 0.12297478
## 133 133 0.38143072 1.1298277 0.217632607 0.44318596
## 134 134 0.35022771 1.0432800 0.215844259 0.32436785
## 135 135 0.16192533 0.9930250 0.268505007 0.36365870
## 136 136 0.23344204 0.5125688 0.315089583 0.46691465
## 137 137 0.43801299 0.9538559 0.041134715 0.16234203
## 138 138 0.37584653 1.0830959 0.196763754 0.33638421
## 139 139 0.52102125 1.1900952 0.000000000 0.39066130
## 140 140 0.85842818 1.1044120 0.049868666 0.00000000
## 141 141 0.40147722 0.5815433 0.180746779 0.10617952
## 142 142 1.12209415 1.2215550 0.341755509 0.50519633
## 143 143 0.43108541 0.4352998 0.209930211 0.42596278
## 144 144 0.30580869 0.9130204 0.375223309 0.18919677
## 145 145 0.36861026 0.6404498 0.277321130 0.03036986
## 146 146 0.59168345 0.9353822 0.310080916 0.24946372
## 147 147 0.39724863 0.6013231 0.163486004 0.14706244
## 148 148 0.11904179 0.8721179 0.229918197 0.33288118
## 149 149 0.24454993 0.7912447 0.194129139 0.34858751
## 150 150 0.30544472 0.4318825 0.247105569 0.38042614
## 151 151 0.36874589 0.9457070 0.326424807 0.58184385
## 152 152 0.77715313 0.3961026 0.500533342 0.08153944
## 153 153 0.51113588 1.0419898 0.364509284 0.39001778
## 154 154 0.09162257 0.6297936 0.151610792 0.05990075
## 155 155 0.00000000 0.0000000 0.018772686 0.27084205
## Generosity Trust
## 1 0.36201224 0.315963835
## 2 0.35528049 0.400770068
## 3 0.47554022 0.153526559
## 4 0.29054928 0.367007285
## 5 0.24548277 0.382611543
## 6 0.47048983 0.282661825
## 7 0.43553972 0.287371516
## 8 0.50000513 0.382816702
## 9 0.38539925 0.384398729
## 10 0.47769925 0.301183730
## 11 0.33008265 0.085242100
## 12 0.21461323 0.100106589
## 13 0.31647232 0.221060365
## 14 0.39257878 0.135638788
## 15 0.42785832 0.298388153
## 16 0.33626917 0.276731938
## 17 0.23150334 0.251343131
## 18 0.28318098 0.318834424
## 19 0.49277416 0.265428066
## 20 0.32666242 0.082287982
## 21 0.36094195 0.324489564
## 22 0.16234989 0.111092761
## 23 0.08810676 0.036872927
## 24 0.10945706 0.059739888
## 25 0.12099043 0.132774115
## 26 0.34596598 0.464307785
## 27 0.57473058 0.153066069
## 28 0.17509693 0.178061873
## 29 0.28348839 0.077223279
## 30 0.21055694 0.070983924
## 31 0.12976231 0.172502428
## 32 0.57212311 0.031612735
## 33 0.25836048 0.063829236
## 34 0.19013357 0.070914097
## 35 0.33047387 0.439299256
## 36 0.14901447 0.046668742
## 37 0.14761601 0.273432255
## 38 0.32529646 0.008964816
## 39 0.22828980 0.215159550
## 40 0.13654448 0.024210852
## 41 0.17266849 0.257042170
## 42 0.41660893 0.065600708
## 43 0.30167422 0.130687982
## 44 0.15011247 0.140134647
## 45 0.07825799 0.089980960
## 46 0.15846597 0.059307806
## 47 0.41598365 0.246528223
## 48 0.17278965 0.028028091
## 49 0.05226382 0.032962881
## 50 0.24001564 0.096581072
## 51 0.12057277 0.163760737
## 52 0.01016466 0.011865643
## 53 0.06943665 0.146096110
## 54 0.15307479 0.073842727
## 55 0.20667437 0.063282669
## 56 0.20877913 0.010091286
## 57 0.13351992 0.004387901
## 58 0.22556072 0.060477726
## 59 0.24992499 0.259270340
## 60 0.20196442 0.119282886
## 61 0.26684570 0.155353352
## 62 0.24264909 0.045128979
## 63 0.12681971 0.047049087
## 64 0.36093375 0.042181555
## 65 0.27125430 0.041237976
## 66 0.09889081 0.183248922
## 67 0.15513751 0.156313822
## 68 0.15207367 0.092610210
## 69 0.04669304 0.099671580
## 70 0.22415066 0.091065913
## 71 0.37446579 0.293933749
## 72 0.19351342 0.099331893
## 73 0.22012588 0.040903781
## 74 0.17217046 0.119803272
## 75 0.07840166 0.036636937
## 76 0.23381834 0.055267781
## 77 0.24800298 0.043103110
## 78 0.31993145 0.057471618
## 79 0.02880684 0.022794275
## 80 0.31544602 0.124348067
## 81 0.61170459 0.015317135
## 82 0.06501963 0.064491123
## 83 0.19791102 0.088174194
## 84 0.03220996 0.087763183
## 85 0.04526934 0.180987507
## 86 0.18696785 0.113945253
## 87 0.00000000 0.043289777
## 88 0.26445076 0.037513830
## 89 0.09510271 0.015869452
## 90 0.32886750 0.000000000
## 91 0.23618887 0.073345453
## 92 0.25376096 0.060277794
## 93 0.29163131 0.282410324
## 94 0.23496805 0.087633237
## 95 0.23094720 0.026121566
## 96 0.24899764 0.146377146
## 97 0.47405455 0.173380390
## 98 0.42858037 0.039439179
## 99 0.39409617 0.072975546
## 100 0.33846423 0.032902289
## 101 0.13936238 0.072509497
## 102 0.04969336 0.086723149
## 103 0.11417317 0.089282602
## 104 0.12863144 0.114381365
## 105 0.11317769 0.011051531
## 106 0.29304090 0.071095176
## 107 0.23629846 0.051306631
## 108 0.38724291 0.048761073
## 109 0.20131294 0.039864216
## 110 0.17225535 0.123717859
## 111 0.07713374 0.093146972
## 112 0.44486031 0.064641319
## 113 0.32222810 0.179436386
## 114 0.83807516 0.188816205
## 115 0.18346889 0.115460448
## 116 0.24958014 0.078213550
## 117 0.19963726 0.107215755
## 118 0.04378538 0.076046787
## 119 0.31265074 0.165455714
## 120 0.49086356 0.073653966
## 121 0.08348809 0.026674422
## 122 0.23153849 0.092226885
## 123 0.20187002 0.136957005
## 124 0.12085213 0.079618134
## 125 0.05400882 0.251666635
## 126 0.24645583 0.060241356
## 127 0.18335420 0.104970247
## 128 0.20123747 0.130061775
## 129 0.38592297 0.068105951
## 130 0.18231745 0.089847520
## 131 0.25692394 0.025336370
## 132 0.27006146 0.023029471
## 133 0.32576606 0.057069719
## 134 0.25086468 0.120328106
## 135 0.22867385 0.138572946
## 136 0.28717047 0.072711654
## 137 0.21611385 0.053581882
## 138 0.18914349 0.095375381
## 139 0.15749727 0.119094640
## 140 0.09792649 0.069720335
## 141 0.31187093 0.061157830
## 142 0.09934845 0.098583199
## 143 0.20794846 0.060929015
## 144 0.20873253 0.067231975
## 145 0.48920378 0.099872150
## 146 0.10412521 0.056767423
## 147 0.28567082 0.116793513
## 148 0.26654989 0.038948249
## 149 0.26481509 0.110937618
## 150 0.19689615 0.095665015
## 151 0.25275603 0.455220014
## 152 0.49366373 0.151347131
## 153 0.35425636 0.066035107
## 154 0.20443518 0.084147945
## 155 0.28087649 0.056565076
md.pattern(happiness_new)
## /\ /\
## { `---' }
## { O O }
## ==> V <== No need for mice. This data set is completely observed.
## \ \|/ /
## `-----'
## Happiness.Rank Economy Family Life.Expectancy Freedom Generosity Trust
## 155 1 1 1 1 1 1 1
## 0 0 0 0 0 0 0
##
## 155 0
## 0
happy.pca <- PCA(happiness_new, graph = F)
eig.val <- get_eigenvalue(happy.pca)
eig.val
## eigenvalue variance.percent cumulative.variance.percent
## Dim.1 3.7738458 53.912084 53.91208
## Dim.2 1.3526400 19.323429 73.23551
## Dim.3 0.6725747 9.608209 82.84372
## Dim.4 0.5434929 7.764184 90.60791
## Dim.5 0.3613563 5.162233 95.77014
## Dim.6 0.1613830 2.305471 98.07561
## Dim.7 0.1347073 1.924390 100.00000
fviz_eig(happy.pca, addlabels = TRUE, ylim = c(0, 60), linecolor = "purple", barfill = "orange", barcolor = "orange")
#Showing the variables
var <- get_pca_var(happy.pca)
fviz_pca_var(happy.pca, col.var = "darkblue")
#Analysis:We see that for instance family, life expectancy and economy are highly correlated. Trust in the government and freedom are also correlated.
#We also see that life expectancy, etc are more correlated with the first dimension whereas freedom, generousity are more correlated with the second dimension.
#Here ,Cos2 shows the quality of representation
fviz_cos2(happy.pca, choice ="var", axes = 1:2, top = 10, color = "dark blue" )
#Contribution of the variables
var$contrib
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## Happiness.Rank 22.932697 0.6789814 0.310774650 0.1793912 0.2806790
## Economy 20.716212 6.7440632 0.001856379 6.5004663 1.0187508
## Family 17.085010 3.5540115 3.460364349 14.4828341 53.2314125
## Life.Expectancy 19.113500 5.4852433 2.941657344 11.0780005 16.6241839
## Freedom 11.316840 16.4889758 4.792429952 44.9297245 18.6687593
## Generosity 1.107816 44.8927351 48.343342584 4.2587796 0.3446663
## Trust 7.727925 22.1559896 40.149574744 18.5708037 9.8315482
#Contribution of the top 5 variables
fviz_contrib(happy.pca, choice = "var", axes = 1, top = 5)
#PCA plot with "fviz_pca_ind"
ind <- get_pca_ind(happy.pca)
ind
## Principal Component Analysis Results for individuals
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for the individuals"
## 2 "$cos2" "Cos2 for the individuals"
## 3 "$contrib" "contributions of the individuals"
happy.pca$ind
## $coord
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## 1 3.59019076 1.08250314 -0.450468480 0.1795351775 0.203717981
## 2 3.65800154 1.49462921 -0.993233171 0.4623440892 0.575769809
## 3 3.22559015 0.87222222 1.240646388 -0.4309720952 -0.057690812
## 4 3.64145132 0.90892328 -1.086410048 0.4358566269 0.242359558
## 5 3.44481314 0.86107541 -1.434504080 0.2696471323 0.451434484
## 6 3.17060061 1.46674977 0.329620821 0.5239736659 0.034982403
## 7 3.29625427 1.34316769 0.132010313 0.3248831025 0.059475006
## 8 3.58837220 2.13705235 -0.105495526 0.6588928161 0.594333348
## 9 3.51091218 1.54878811 -0.744266994 0.6624902662 0.329475308
## 10 3.37804136 1.56596767 0.299356612 0.4719575501 0.207386088
## 11 1.89759657 -0.54176346 1.081099812 0.3085747334 -0.263923728
## 12 1.85267489 -0.35751917 0.105847457 -0.9112901797 -0.468185291
## 13 2.80142571 0.36293579 -0.041535574 0.0660987685 -0.088667493
## 14 2.40693880 0.20949050 0.919669437 0.1301033239 -0.184706513
## 15 3.27581897 1.20653752 0.100411327 0.4350945432 0.434518627
## 16 2.91448110 0.71896509 -0.286965843 0.3203839472 0.171089074
## 17 2.69536078 0.02130748 -0.630856935 0.1802297838 0.060421297
## 18 3.38319960 0.55293877 -0.856260913 0.5060763390 0.003147218
## 19 2.84789300 1.31155876 0.690162219 0.7081314382 0.426159863
## 20 1.42378839 -0.47957501 1.040839045 0.4511586520 -0.352769036
## 21 2.83362777 1.31905581 -0.718936916 0.6170872714 -0.230884041
## 22 1.16828884 -0.80289677 -0.130939210 -0.5183208187 0.252331636
## 23 1.54122275 -1.66106879 -0.031409762 -0.8469552295 -0.453559231
## 24 1.33613264 -1.27872740 -0.107438985 -0.9259930732 -0.223273469
## 25 1.01198728 -0.94591337 -0.523979955 0.0764714232 -0.357585894
## 26 3.63004496 1.42506958 -1.373565987 1.6932889379 0.192808428
## 27 2.61190764 1.48167383 1.696817921 -0.0231876923 -0.134589630
## 28 1.89913318 -0.18912330 -0.638043896 -0.6076488590 -0.190227267
## 29 0.65629739 0.24877686 0.394518179 -0.9074308031 -0.321921960
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## 31 1.99858112 -0.97216166 -0.608968162 0.1299486787 -0.237954031
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## 41 1.92182469 0.03022989 -1.206879543 0.1180054614 0.013500606
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## 99 -0.82518425 0.99127696 0.967807729 -0.4691773930 0.021402880
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## 101 -0.62590514 -0.34889118 -0.509659696 -1.4053914310 0.730822047
## 102 -1.10298496 -1.29603823 -0.812031993 0.6258845868 -0.755129270
## 103 -1.12386552 -1.01754880 -0.289242696 0.2678328590 0.267323039
## 104 -1.00847591 -0.77268979 -0.551301795 0.5872857485 -0.117065699
## 105 -0.13377534 -1.88892050 0.421724839 -0.1183105031 0.284555941
## 106 -2.58304007 0.85274993 0.138400640 -0.5911599516 0.720939804
## 107 -2.04807118 0.56064248 -0.130672107 -0.9131920434 -0.050635399
## 108 -1.11337347 0.09724649 1.139486494 1.5057013211 -1.081961475
## 109 -0.86822829 -0.56273437 0.166226661 0.4998416173 -1.456540265
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## 113 -2.09542108 2.01223295 -0.633011472 -0.5940268534 0.177606159
## 114 -0.55362636 4.18509082 2.301291664 0.2428907587 0.445493421
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## 116 -1.59801531 0.64710572 -0.160017546 -0.7659567429 -0.128383765
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## 119 -1.80570273 1.40961136 -0.323185571 0.0793966317 -0.121188539
## 120 0.21528667 1.34286579 1.395983630 -0.4544530041 -0.389465116
## 121 -1.52012400 -1.66926747 -0.042387762 0.6345596825 -0.301365928
## 122 -1.44359116 0.52754855 -0.380600796 -0.0622640349 -1.122448135
## 123 -1.91947978 -0.50949979 -0.053311498 0.7446006108 1.731750643
## 124 -1.84081082 -0.07182344 -0.895720510 -0.4533989124 -0.627043054
## 125 -1.27292896 -0.21729760 -2.045118355 1.6797523017 -1.080350157
## 126 -2.65204050 0.20778502 0.358213643 -0.5507056183 1.299603045
## 127 -1.97878307 0.04408718 -0.338621371 -0.6405058043 1.286690894
## 128 -2.21863596 0.92398100 -0.944447907 -0.7301892838 0.183521259
## 129 -0.98664032 1.61962302 0.468252592 -1.1402156382 -0.861657736
## 130 -2.38288637 -1.00738937 0.216337912 0.9761325828 1.659979994
## 131 -2.06825748 0.38969089 0.189404790 -0.5482763348 -0.562220081
## 132 -1.23289328 -1.13010794 1.249384681 0.6489482620 1.093590998
## 133 -1.98797544 1.02447547 0.427990946 -1.0201179832 0.366250144
## 134 -2.31326602 0.70197644 -0.239692452 -0.2389774587 0.663879034
## 135 -2.38235317 0.88208929 -0.519894531 -0.3441745817 0.424673978
## 136 -2.82535393 1.37605246 -0.236803692 -0.2487255663 -1.372591312
## 137 -3.27732651 -0.03822839 0.045523472 0.0791600208 0.966410701
## 138 -2.39587408 0.29508097 -0.413918674 -0.5542110947 0.657916265
## 139 -2.29621034 0.43136647 -0.881528448 -1.0754446414 1.139016662
## 140 -3.03331543 -1.35616873 -0.333268822 0.7646540489 1.704788634
## 141 -3.66950439 0.45874502 0.430814357 1.1833148731 0.017965380
## 142 -0.84248834 -0.36891079 -0.963342827 -0.9577211259 0.068544153
## 143 -3.17842908 0.86061040 -0.650448629 -0.1555834596 -1.380039443
## 144 -2.84447303 -0.14818599 0.091236800 0.4565749832 0.287424484
## 145 -3.40970772 1.21031606 1.321224151 2.0017694650 0.430445911
## 146 -2.61896031 -0.67830078 -0.511140740 0.0353886807 0.138118495
## 147 -3.51760075 0.71529947 -0.119647128 1.1488942687 0.148827434
## 148 -3.12683266 0.69296980 0.221843574 -0.4791033551 0.006111377
## 149 -2.95282393 1.07422565 -0.341337722 -0.1117135480 0.009383819
## 150 -3.34166655 0.89925518 -0.840850001 0.1664685320 -1.180117633
## 151 -0.91184753 2.94080028 -2.705502585 0.3465987794 0.533143237
## 152 -2.72121780 1.31022990 0.937854796 2.8922433485 -0.653969605
## 153 -1.98905603 0.93259564 0.620516489 -0.2784678450 0.075320297
## 154 -4.25436160 0.11692887 -0.204025100 1.0423187556 0.439068024
## 155 -5.06055612 1.54782089 -0.450661765 0.6919441788 -1.574791689
##
## $cos2
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## 1 8.944560e-01 8.131717e-02 1.408162e-02 2.236779e-03 2.879935e-03
## 2 7.777504e-01 1.298433e-01 5.733963e-02 1.242462e-02 1.926860e-02
## 3 8.051190e-01 5.887027e-02 1.191071e-01 1.437276e-02 2.575461e-04
## 4 8.531622e-01 5.315413e-02 7.593992e-02 1.222278e-02 3.779226e-03
## 5 7.874624e-01 4.920179e-02 1.365533e-01 4.824920e-03 1.352346e-02
## 6 7.932040e-01 1.697516e-01 8.572966e-03 2.166311e-02 9.656085e-05
## 7 8.476659e-01 1.407485e-01 1.359562e-03 8.234506e-03 2.759639e-04
## 8 7.038552e-01 2.496426e-01 6.083541e-04 2.373110e-02 1.930850e-02
## 9 7.776893e-01 1.513388e-01 3.494811e-02 2.769014e-02 6.848760e-03
## 10 8.023333e-01 1.724211e-01 6.300900e-03 1.566141e-02 3.024016e-03
## 11 6.502810e-01 5.300449e-02 2.110692e-01 1.719547e-02 1.257912e-02
## 12 6.737322e-01 2.508924e-02 2.199124e-03 1.630055e-01 4.302535e-02
## 13 9.782810e-01 1.641969e-02 2.150531e-04 5.446182e-04 9.800184e-04
## 14 8.286471e-01 6.277232e-03 1.209771e-01 2.421120e-03 4.879822e-03
## 15 8.528629e-01 1.156967e-01 8.013179e-04 1.504550e-02 1.500570e-02
## 16 9.196463e-01 5.596467e-02 8.915768e-03 1.111322e-02 3.169151e-03
## 17 9.397153e-01 5.872547e-05 5.147831e-02 4.201609e-03 4.722180e-04
## 18 8.896981e-01 2.376520e-02 5.699013e-02 1.990763e-02 7.699119e-07
## 19 7.373968e-01 1.563974e-01 4.330682e-02 4.559127e-02 1.651198e-02
## 20 5.008789e-01 5.682703e-02 2.676757e-01 5.029218e-02 3.074842e-02
## 21 7.273753e-01 1.576156e-01 4.682249e-02 3.449579e-02 4.829040e-03
## 22 4.720265e-01 2.229387e-01 5.929327e-03 9.291017e-02 2.201960e-02
## 23 3.847005e-01 4.468555e-01 1.597796e-04 1.161750e-01 3.331654e-02
## 24 3.886254e-01 3.559492e-01 2.512788e-03 1.866584e-01 1.085191e-02
## 25 3.520654e-01 3.075926e-01 9.438491e-02 2.010350e-03 4.395760e-02
## 26 6.543511e-01 1.008461e-01 9.368841e-02 1.423800e-01 1.846030e-03
## 27 5.719749e-01 1.840630e-01 2.413970e-01 4.507922e-05 1.518744e-03
## 28 7.924174e-01 7.858384e-03 8.944258e-02 8.112385e-02 7.950395e-03
## 29 1.890039e-01 2.715747e-02 6.829727e-02 3.613236e-01 4.547477e-02
## 30 6.146074e-01 8.676771e-02 1.359736e-02 1.933680e-01 6.979361e-02
## 31 7.366049e-01 1.742882e-01 6.838805e-02 3.114116e-03 1.044184e-02
## 32 2.419489e-01 1.531076e-01 5.208846e-01 6.301460e-02 8.234374e-03
## 33 5.023080e-01 2.808881e-01 1.767194e-01 2.031722e-02 3.670749e-03
## 34 5.821389e-01 3.452145e-01 5.246339e-02 1.350131e-03 3.139993e-04
## 35 5.807079e-01 1.396616e-01 1.469461e-01 8.180942e-02 9.503879e-04
## 36 2.264808e-01 3.339006e-01 3.041668e-03 3.492361e-01 2.277707e-03
## 37 5.077552e-01 7.416829e-03 3.445759e-01 4.933282e-02 1.698731e-02
## 38 2.615136e-01 1.395144e-02 2.900879e-01 2.206466e-01 1.284218e-02
## 39 7.237842e-01 1.645744e-04 9.459459e-02 2.669771e-02 4.186508e-03
## 40 1.605576e-01 7.338349e-01 6.372498e-02 2.111559e-03 2.633357e-02
## 41 6.948328e-01 1.719198e-04 2.740190e-01 2.619733e-03 3.428935e-05
## 42 2.689769e-01 9.361743e-03 5.952906e-01 8.293597e-03 1.650665e-03
## 43 2.295173e-01 7.552830e-02 8.788474e-02 3.470228e-02 5.360886e-04
## 44 4.304985e-01 1.803491e-01 1.343335e-01 3.670514e-02 1.386882e-02
## 45 1.320012e-02 4.181363e-01 1.712287e-01 1.125343e-01 5.587458e-02
## 46 4.850525e-01 2.750321e-01 2.189060e-03 2.150250e-01 2.029563e-02
## 47 3.012991e-01 4.766177e-01 8.071873e-05 1.313554e-01 6.960950e-02
## 48 1.759597e-01 6.613430e-01 1.199891e-01 3.654069e-02 2.031989e-03
## 49 6.303366e-02 7.499973e-01 6.705828e-03 1.167466e-01 2.576738e-02
## 50 6.944245e-03 8.871081e-02 1.933033e-02 3.980908e-01 2.141517e-01
## 51 6.787283e-01 1.729032e-01 5.951351e-02 2.285836e-04 1.926935e-02
## 52 1.222026e-02 9.352111e-01 1.695580e-06 2.807335e-03 3.745209e-02
## 53 1.847004e-04 5.404063e-01 1.560084e-01 1.766883e-01 1.351527e-02
## 54 1.633842e-01 7.591809e-01 1.243963e-02 5.684769e-09 5.017531e-02
## 55 1.055071e-01 4.528009e-01 7.922213e-02 2.745260e-01 8.583691e-02
## 56 1.094660e-01 3.916980e-01 2.227277e-01 1.921632e-05 1.897737e-02
## 57 4.312286e-02 5.325957e-01 1.069620e-02 9.309530e-02 3.058904e-01
## 58 6.614848e-03 7.460523e-03 1.739844e-03 8.267170e-01 8.183768e-02
## 59 2.812308e-01 6.101313e-02 1.460805e-01 6.513427e-04 4.934530e-01
## 60 6.307218e-01 2.383727e-01 3.091056e-03 3.987706e-02 2.857863e-02
## 61 6.839214e-01 5.111300e-03 3.843934e-04 9.314200e-02 1.733839e-01
## 62 5.200865e-01 7.958470e-02 7.633791e-02 1.729437e-01 7.008402e-02
## 63 2.237208e-02 5.889963e-01 7.337152e-03 2.168580e-01 1.462581e-01
## 64 1.768327e-01 1.269671e-02 5.326481e-01 5.183120e-02 1.692474e-01
## 65 1.397392e-01 2.543939e-01 2.507331e-01 1.227062e-01 2.188109e-01
## 66 4.920387e-01 1.774614e-01 2.286329e-01 1.932759e-02 2.445314e-02
## 67 1.546224e-01 4.260822e-01 3.282738e-02 6.380963e-02 3.049084e-01
## 68 1.240874e-01 3.247242e-01 6.628548e-02 4.431112e-01 1.670969e-02
## 69 1.622388e-02 8.582948e-01 9.054789e-02 4.261017e-03 2.937169e-02
## 70 2.085272e-01 5.809569e-02 3.154254e-02 4.848022e-01 1.368972e-01
## 71 5.941135e-01 8.335180e-02 4.272647e-03 2.078097e-01 9.357506e-03
## 72 1.286322e-02 2.329258e-02 8.261443e-02 8.406147e-01 3.966426e-02
## 73 2.573431e-02 5.756973e-01 2.253405e-01 1.299934e-01 3.044344e-02
## 74 7.546538e-02 5.210651e-01 2.284217e-01 6.632422e-02 4.157664e-03
## 75 5.974294e-04 9.035607e-01 7.490309e-03 5.830170e-02 2.911004e-02
## 76 9.182535e-02 1.172880e-01 1.642395e-01 4.684254e-01 6.850722e-03
## 77 3.765898e-02 2.916330e-01 1.611729e-01 3.315819e-01 1.568645e-01
## 78 1.965187e-01 4.500168e-02 5.357826e-01 1.627990e-01 1.342700e-02
## 79 2.354454e-03 5.732368e-01 5.057715e-02 1.037720e-01 2.272426e-01
## 80 4.864658e-01 4.933896e-02 4.413952e-03 2.595648e-01 3.720269e-02
## 81 4.376823e-04 2.292250e-01 7.288956e-01 6.623206e-03 2.508420e-04
## 82 1.420649e-02 7.916289e-01 1.260240e-06 3.768821e-02 1.505699e-01
## 83 2.047207e-02 5.451776e-01 5.572729e-02 3.151633e-01 5.578944e-02
## 84 1.879455e-01 1.780806e-01 2.616175e-01 1.282351e-03 3.477472e-01
## 85 6.053615e-05 2.427845e-01 7.350194e-01 5.781417e-03 7.596485e-05
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## 113 4.649135e-01 4.287321e-01 4.242801e-02 3.736299e-02 3.339993e-03
## 114 1.300011e-02 7.428879e-01 2.246244e-01 2.502278e-03 8.417752e-03
## 115 6.685198e-01 3.067991e-03 5.354412e-02 7.363881e-02 6.514802e-02
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## 133 6.137480e-01 1.629938e-01 2.844705e-02 1.616102e-01 2.083165e-02
## 134 8.297009e-01 7.640389e-02 8.907977e-03 8.854912e-03 6.833580e-02
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## 139 5.869219e-01 2.071332e-02 8.650269e-02 1.287458e-01 1.444166e-01
## 140 5.904246e-01 1.180203e-01 7.127195e-03 3.751969e-02 1.864964e-01
## 141 8.643146e-01 1.350827e-02 1.191345e-02 8.987889e-02 2.071714e-05
## 142 1.531922e-01 2.937322e-02 2.002952e-01 1.979644e-01 1.014028e-03
## 143 7.572629e-01 5.551813e-02 3.171375e-02 1.814465e-03 1.427594e-01
## 144 9.171970e-01 2.489278e-03 9.436243e-04 2.363106e-02 9.364973e-03
## 145 6.078892e-01 7.659276e-02 9.127318e-02 2.095165e-01 9.687848e-03
## 146 8.899059e-01 5.969403e-02 3.389747e-02 1.624856e-04 2.475084e-03
## 147 8.594939e-01 3.554066e-02 9.943843e-04 9.168744e-02 1.538564e-03
## 148 9.094490e-01 4.466812e-02 4.577863e-03 2.135145e-02 3.474139e-06
## 149 8.684229e-01 1.149337e-01 1.160446e-02 1.242991e-03 8.770320e-06
## 150 7.915556e-01 5.732197e-02 5.011783e-02 1.964353e-03 9.872015e-02
## 151 4.487506e-02 4.667579e-01 3.950540e-01 6.483577e-03 1.534081e-02
## 152 3.910765e-01 9.066302e-02 4.645221e-02 4.417787e-01 2.258657e-02
## 153 6.655154e-01 1.463022e-01 6.476953e-02 1.304410e-02 9.543070e-04
## 154 9.147652e-01 6.910101e-04 2.103817e-03 5.490880e-02 9.743264e-03
## 155 7.968166e-01 7.454239e-02 6.319218e-03 1.489718e-02 7.716282e-02
##
## $contrib
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## 1 2.203531e+00 0.5589132348 1.946510e-01 3.826252e-02 7.409543e-02
## 2 2.287557e+00 1.0654988855 9.463030e-01 2.537494e-01 5.918757e-01
## 3 1.778699e+00 0.3628610574 1.476467e+00 2.204818e-01 5.942183e-03
## 4 2.266904e+00 0.3940401888 1.132180e+00 2.255079e-01 1.048704e-01
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## 8 2.201299e+00 2.1782934594 1.067569e-02 5.153520e-01 6.306565e-01
## 9 2.107289e+00 1.1441160570 5.313557e-01 5.209949e-01 1.938110e-01
## 10 1.950806e+00 1.1696384814 8.596180e-02 2.644115e-01 7.678775e-02
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## 12 5.867898e-01 0.0609655847 1.074703e-02 9.857977e-01 3.913526e-01
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## 74 5.215003e-03 0.1004617520 8.857022e-02 3.182506e-02 3.000577e-03
## 75 5.997402e-04 2.5306719393 4.219101e-02 4.063948e-01 3.051884e-01
## 76 1.896308e-02 0.0675774708 1.903126e-01 6.717026e-01 1.477513e-02
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## 120 7.923525e-03 0.8601046284 1.869341e+00 2.451616e-01 2.708132e-01
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## 125 2.770081e-01 0.0225214283 4.012035e+00 3.349384e+00 2.083828e+00
## 126 1.202387e+00 0.0205927595 1.230870e-01 3.600091e-01 3.015464e+00
## 127 6.693920e-01 0.0009270660 1.099908e-01 4.869906e-01 2.955842e+00
## 128 8.415041e-01 0.4072041026 8.556257e-01 6.329149e-01 6.013197e-02
## 129 1.664186e-01 1.2511631522 2.103237e-01 1.543293e+00 1.325569e+00
## 130 9.707129e-01 0.4840394941 4.489449e-02 1.131077e+00 4.919698e+00
## 131 7.312963e-01 0.0724313475 3.441198e-02 3.568399e-01 5.643459e-01
## 132 2.598574e-01 0.6091523416 1.497339e+00 4.999132e-01 2.135220e+00
## 133 6.756257e-01 0.5005980931 1.757102e-01 1.235308e+00 2.394905e-01
## 134 9.148193e-01 0.2350342401 5.511077e-02 6.779354e-02 7.868838e-01
## 135 9.702786e-01 0.3711172702 2.592736e-01 1.406151e-01 3.219912e-01
## 136 1.364677e+00 0.9031420188 5.379040e-02 7.343706e-02 3.363684e+00
## 137 1.836215e+00 0.0006970406 1.987918e-03 7.438515e-03 1.667463e+00
## 138 9.813233e-01 0.0415306262 1.643455e-01 3.646069e-01 7.728122e-01
## 139 9.013791e-01 0.0887521394 7.454190e-01 1.372937e+00 2.316290e+00
## 140 1.572966e+00 0.8772300913 1.065411e-01 6.940718e-01 5.188882e+00
## 141 2.301966e+00 0.1003757490 1.780361e-01 1.662169e+00 5.762428e-04
## 142 1.213422e-01 0.0649125672 8.902040e-01 1.088811e+00 8.388292e-03
## 143 1.727067e+00 0.3532638885 4.058390e-01 2.873434e-02 3.400288e+00
## 144 1.383209e+00 0.0104737060 7.984871e-03 2.474564e-01 1.474960e-01
## 145 1.987552e+00 0.6986885973 1.674483e+00 4.756665e+00 3.308033e-01
## 146 1.172579e+00 0.2194475287 2.506160e-01 1.486631e-03 3.405935e-02
## 147 2.115326e+00 0.2440404828 1.373195e-02 1.566876e+00 3.954565e-02
## 148 1.671450e+00 0.2290417648 4.720864e-02 2.724789e-01 6.668235e-05
## 149 1.490594e+00 0.5503983261 1.117626e-01 1.481447e-02 1.572143e-04
## 150 1.909020e+00 0.3857020654 6.782110e-01 3.289567e-02 2.486470e+00
## 151 1.421440e-01 4.1249351728 7.021400e+00 1.426029e-01 5.074818e-01
## 152 1.265933e+00 0.8188061175 8.437213e-01 9.929882e+00 7.635682e-01
## 153 6.763604e-01 0.4148325501 3.693469e-01 9.205016e-02 1.012877e-02
## 154 3.094233e+00 0.0065212314 3.992960e-02 1.289661e+00 3.441885e-01
## 155 4.378049e+00 1.1426874900 1.948181e-01 5.683509e-01 4.427708e+00
##
## $dist
## 1 2 3 4 5 6 7
## 3.7961035 4.1478569 3.5948364 3.9423821 3.8819573 3.5599926 3.5802087
## 8 9 10 11 12 13 14
## 4.2771633 3.9812266 3.7712694 2.3531703 2.2571248 2.8323526 2.6441144
## 15 16 17 18 19 20 21
## 3.5471560 3.0391409 2.7804734 3.5867928 3.3164468 2.0117734 3.3224888
## 22 23 24 25 26 27 28
## 1.7004624 2.4848721 2.1433050 1.7055458 4.4875220 3.4535786 2.1334301
## 29 30 31 32 33 34 35
## 1.5096120 1.8913199 2.3286515 3.1644011 2.0317777 2.4076524 4.2138451
## 36 37 38 39 40 41 42
## 1.6802230 2.3449152 2.0540685 2.1244585 2.2945241 2.3055451 1.8151559
## 43 44 45 46 47 48 49
## 1.2256896 1.2886774 1.5230353 1.9055964 2.8612398 2.4617638 2.1967844
## 50 51 52 53 54 55 56
## 1.2966464 2.4465352 2.7794356 1.9108396 1.5677954 2.2211214 1.8100399
## 57 58 59 60 61 62 63
## 1.7483685 1.3670924 1.8632259 1.1586555 1.6201893 2.1256675 1.2989620
## 64 65 66 67 68 69 70
## 1.4624049 1.8066994 1.9702201 1.5199751 1.1072619 1.8706845 1.2703908
## 71 72 73 74 75 76 77
## 2.9515503 1.3751355 1.6665657 0.6357865 2.4232389 1.0990845 1.7294346
## 78 79 80 81 82 83 84
## 1.3197147 2.1233045 2.3743331 2.9523132 2.4476885 1.6389632 2.2106725
## 85 86 87 88 89 90 91
## 1.6697843 1.3345677 3.1908318 1.4565673 2.2125182 2.0946386 0.9519184
## 92 93 94 95 96 97 98
## 0.9949776 3.9650746 1.3902687 2.3873386 1.2983809 2.0150897 2.0441449
## 99 100 101 102 103 104 105
## 1.7889742 1.6271956 2.0611503 2.1284793 1.6995077 1.5172962 2.1378658
## 106 107 108 109 110 111 112
## 3.0234079 2.4102446 2.5106647 1.9217531 2.1105581 1.9227011 2.3266386
## 113 114 115 116 117 118 119
## 3.0731606 4.8556037 1.6640172 1.9224553 1.4893784 2.1606797 2.3676957
## 120 121 122 123 124 125 126
## 2.3705754 2.4218230 1.9796189 2.7404955 2.2592936 3.1713533 3.1210155
## 127 128 129 130 131 132 133
## 2.4717214 2.7883193 2.5350895 3.2326160 2.3032299 2.5862987 2.5375579
## 134 135 136 137 138 139 140
## 2.5395970 2.7393995 3.4577211 3.4660498 2.6053223 2.9972400 3.9476201
## 141 142 143 144 145 146 147
## 3.9470395 2.1525118 3.6524912 2.9700961 4.3732562 2.7762392 3.7942431
## 148 149 150 151 152 153 154
## 3.2788041 3.1686328 3.7559724 4.3044701 4.3514342 2.4381924 4.4481515
## 155
## 5.6691645
#Plotting the graph
fviz_pca_ind (happy.pca, pointsize = "cos2", pointshape = 22, fill = "blue", repel = TRUE)
#Method to show only the 50 countries best represented.
plot(happy.pca, select = "cos2 50", cex=1, col.ind = "darkblue", title = "50 countries with highest cos2", cex.main=2, col.main= "darkblue")
